In causal learning, we aim to estimate the effect of our actions on the world. For example, we may be interested in measuring the impact of flight ticket prices on sales [1]}, [2]}, or the effect of grade retention on the cognitive development [3]}.
We refer to our action as a treatment, which results in a particular outcome.
It is often impossible to determine the effect of treatment on outcome from observational data alone, however,
since the observed joint distribution of treatment and outcome can depend on a common confounder which influences both, and which might not be observed. In our example on sales of airplane tickets given price, the two might even be positively correlated in some circumstances, such as the simultaneous increase in sales and prices during the holiday season. This does not mean that raising the price causes sales to increase. In this context, peoples' desire to fly is a confounder, since it affects both the number of ticket sales and the prices people are willing to accept. Thus, we need to correct the bias caused by the confouder.
